Face recognition comprises of methods enabling the identification of a person's face based on a digital photography. The problem of face recognition can be cast as a standard pattern classification or machine learning problem: Given a set of face images labeled with the person's identity (the gallery set) and an unlabeled set of face images from the same group of people (the probe set), the task is to identify each person in the probe images.
Face recognition can be summarized in the following steps:
1. Face detection—the face is located in the image.
2. A collection of descriptive measurements known as a feature vector is extracted from each image.
3. A classifier is trained to assign to each feature vector a label with a person's identity (classifiers are simply mathematical functions which, given a feature vector, return an index corresponding to a subject's identity).
The methods of getting feature vectors:                Geometric feature based methods        Appearance-based methods        
In Geometric feature based methods properties and relations (e.g., distances and angles) between facial features such as eyes, mouth, nose, and chin have been used to perform recognition. It is claimed that methods for face recognition based on finding local image features and inferring identity by the geometric relations of these features are often ineffective. Appearance based methods—use of low-dimensional representations of images of objects or faces to perform recognition. (e.g. SLAM and Eigenfaces). Here the feature vector used for classification is a linear projection of the face image into a lower-dimensional linear subspace. However the appearance based methods suffer from an important drawback: recognition of a face under a particular lighting condition, pose, and expression can be performed reliably provided the face has been previously seen under similar circumstances.
If the gallery set was very large, appearance based classifier will perform well. But the gallery set is limited for practical reasons. To overcome this limitation, 3-D face recognition is used. 3-D recognition builds face recognition systems that use a handful of images acquired at enrollment time to estimate models of the 3-D shape of each face. 3-D models of the face shape can be estimated by a variety of methods, for example Photometric stereopsis can be used to create a 3D model (shape and reflectance) of the face from a few 2D images. The 3D face models generated using one of the above indicated methods are usually stored in Wavefront's OBJ file format. The OBJ file format is a simple data-format that represents 3D geometry alone—namely, the position of each vertex, the UV position of each texture coordinate vertex, normals, and the faces that make each polygon defined as a list of vertices, and texture vertices. An OBJ file is usually small in size. In [A. Georghiades, P. Belhumeur, D. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23, 6 (June 2001), pages 643-660.] is presented a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. The method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. It can be seen a Limited gallery set used by photometric stereopsis to create a synthetic extended gallery set with variable illumination and pose.
Several over-the-top (OTT) players are using face recognition:                Facebook's automatic tag suggestion feature uses face recognition to suggest people a user might want to tag in their photos.        Security: Face recognition could one day replace password logins on favourite apps—e.g. logging in to Twitter with the face. This is currently an academic effort e.g. in 2010 Manchester University researchers were working on creating consumer-focused face recognition technology for security applications.        An application called SocialCamera allows users to snap a picture with their Android phone, instantly recognize their Facebook friends in the frame, tag the photos and post them to the Web.        
The invention that will be described in this application enables an operator scenario as well as a non-operator (OTT) scenario for Face Recognition service and hence is applicable for different business scenarios.
Analysts point to the fact that face recognition technology has emerged as the fastest growing technology among the biometric technologies accepted worldwide and will continue to follow the same trend in future by growing at a CAGR of around 31% during 2011-2013. Currently operator don't offer face recognition services, but they would like to enter this market at some point. To be able to do so, some challenges need to be overcome:                Large database requirement: For a large operator with many subscribers a large galley set is required to be maintained        Centralized repository bottleneck: Traditionally the galley set is stored in a central repository. Such a central repository presents problems:                    Search bottleneck: each face image will have to be matched across all the whole gallery set faces and then across all variations of illumination and pose this is extremely time consuming and negatively impacts end user application performance.            Synthetic face generation bottleneck: there is a computational cost for the synthetic generation.                        Transmission cost: Moving the raw face image data from the terminal to the central server and back increases the amount of data transported via the network.        System responsiveness and user QoE: Moving the raw face image data from the terminal to the central server and back introduces delays due to RTT. This leads to a perceived sluggishness of the system which negatively impacts user QoE.        